XAI-KG: knowledge graph to support XAI and decision-making in manufacturing

TL;DR

This paper introduces XAI-KG, a domain-specific knowledge graph and ontology designed to enhance explainability and decision-making in manufacturing demand forecasting by collecting user feedback and improving model explanations.

Contribution

The paper presents a novel ontology and knowledge graph tailored for demand forecasting, enabling better assessment of explanation quality and decision support in manufacturing.

Findings

Validated on real-world manufacturing data

Improves understanding of forecast explanations

Supports feedback-driven model enhancement

Abstract

The increasing adoption of artificial intelligence requires accurate forecasts and means to understand the reasoning of artificial intelligence models behind such a forecast. Explainable Artificial Intelligence (XAI) aims to provide cues for why a model issued a certain prediction. Such cues are of utmost importance to decision-making since they provide insights on the features that influenced most certain forecasts and let the user decide if the forecast can be trusted. Though many techniques were developed to explain black-box models, little research was done on assessing the quality of those explanations and their influence on decision-making. We propose an ontology and knowledge graph to support collecting feedback regarding forecasts, forecast explanations, recommended decision-making options, and user actions. This way, we provide means to improve forecasting models, explanations, and recommendations of decision-making options. We tailor the knowledge graph for the domain of demand forecasting and validate it on real-world data.